Super-Resolving Blurry Images with Events
This addresses the challenge of enhancing blurry, low-resolution images for applications like photography or surveillance, though it appears incremental with hybrid methods.
The paper tackles the problem of super-resolution from motion-blurred images by introducing EBSR-Net, which uses events to mitigate motion blur and improve high-resolution prediction, achieving state-of-the-art performance.
Super-resolution from motion-blurred images poses a significant challenge due to the combined effects of motion blur and low spatial resolution. To address this challenge, this paper introduces an Event-based Blurry Super Resolution Network (EBSR-Net), which leverages the high temporal resolution of events to mitigate motion blur and improve high-resolution image prediction. Specifically, we propose a multi-scale center-surround event representation to fully capture motion and texture information inherent in events. Additionally, we design a symmetric cross-modal attention module to fully exploit the complementarity between blurry images and events. Furthermore, we introduce an intermodal residual group composed of several residual dense Swin Transformer blocks, each incorporating multiple Swin Transformer layers and a residual connection, to extract global context and facilitate inter-block feature aggregation. Extensive experiments show that our method compares favorably against state-of-the-art approaches and achieves remarkable performance.